Method and apparatus for generating reliability information for channel decoding in a radio receiver

ABSTRACT

An equalizer of a radio receiver for generating reliability information (q) that specifies probabilities of a received data symbol (z) being based on a particular transmitted value. The reliability information (q) is determined for each received data symbol (z) by evaluating trellis state transitions through use of a particular metric, both trellis-based symbols and decision-feedback symbols being evaluated. The decision-feedback symbols used are state-independent symbols that have previously been decided by means of a hard decision.

RELATED APPLICATIONS

This continuing patent application claims priority under 35 U.S.C. §120from International Application Serial No. PCT/DE00/02468, which wasfiled on Jul. 27, 2000.

BACKGROUND OF THE INVENTION

The present invention relates to a method for generating reliabilityinformation for a channel decoder of a radio receiver, particularly amobile radio receiver, and to a corresponding radio receiver.

Transmission channels in mobile radio systems are characterized by theirtime-dependent multipath reception, which leads to intersymbolinterference in the case of digital transmission systems. In order to beable to control such intersymbol interference, equalization of thereceived data is required at the receiving end. At the transmitting end,the data to be transmitted are transmitted interleaved and channel-codeddue to the rapidly changing transmission conditions and for suppressingadjacent-channel and co-channel interference.

For the channel decoding at the receiving end, it is desirable to haveinformation that specifies the reliability of the equalization performedby the equalizer. This reliability information is information that isobtained by a so-called soft decision. In contrast to a hard decision inwhich only a fixed decision threshold is used, a multiplicity ofdecision thresholds is used in the case of a soft decision, whichdistinctly increases the reliability of the decision. Equalizers asused, for example, in GSM receivers and also provided in accordance withthe future expansion of the GSM mobile radio standard, EDGE, thereforemust adequately equalize the received signal on the one hand, and, onthe other hand, provide the aforementioned reliability information.

Many different methods for generating the aforementioned reliabilityinformation are known and in mobile radio systems, algorithms arefrequently used which are based on a so-called Maximum LikelihoodSequence Estimation (MLSE) described, for example, in “DigitalCommunications”, Proakis, J. G., McGraw-Hill, New York, 1983. The mostwidely used implementation of this method is the Viterbi algorithm withwhich the aforementioned reliability information is obtained with theaid of trellis diagrams, in the form of probabilities of whether areceived symbol is based on a transmitted ‘0’ or a transmitted ‘1’.

However, since this (optimum) algorithm is very complex and, as aresult, very computationally intensive, and requires very large storagespace, various sub-optimal methods have been developed that providereliability information for the channel decoder in a simpler manner.

Such a sub-optimal method is described, among other things, in “Optimumand Sub-optimum Detection of Coded Data Disturbed by Time-VaryingIntersymbol Interference”, Wolfgang Koch and Alfred Baier, 1990 IEEE.According to this method called “Reduced State Soft Decision Equalizer”,the reliability information is generated symbol by symbol in theequalizer. The corresponding algorithm is very similar to a harddecision Viterbi algorithm but it generates the reliability informationin a distinctly simpler manner, the reliability information for areceived symbol at time μ−L being determined at a time μ. L heredesignates the length of the observation period and corresponds to atleast the length of the channel impulse response of the transmissionchannel. The reliability information is determined by determining thebest “one path” of the trellis diagram (i.e., the best or mostadvantageous path having the value ‘1’ at time μ−L) and the best “zeropath” (i.e., the best or most advantageous path having the value ‘1’ attime μ−L) by means of a trellis diagram. These two paths of the trellisdiagram are determined by means of metrics that are calculated for theindividual state transitions in the trellis diagram. In this method, inparticular, the so-called “matched-filter” metric is used. Finally, thereliability information is obtained by putting the metrics calculatedfor the best “one path” and best “zero path” in this manner in relationto one another. To reduce the computational expenditure and the storagerequirement, a trellis having a reduced number of states is used forcalculating the individual metrics. A trellis-based equalization is onlystarted for elements 0 . . . L′ (L′<L) of the channel impulse responsewhereas the remaining elements L′+1 . . . L are only included in thetrellis-based equalization in a decision-feedback manner. The principlesof this decision feedback (Decision Feedback Sequence Estimation) can befound, for example, in the paper “Reduced-state Sequence Estimation withSet Partitioning and Decision Feedback”, Vedat Eyuboglu and ShahidQureshi, 1988 IEEE.

In the procedure described above, the equalizer must determine the best“one-path” and “zero path” with reference to time μ−L′ at each time μand calculate from these determinations the reliability information forthe received symbol at time μ−L′. In this process, the branch metricsinclude bits at times μ . . . μ−L′ and bits at times μ−L′−1 . . . μ−L,the latter bits, as already described, being included in the metricscalculation as decision feedback. These latter bits are obtained fromthe individual so-called “survivor” paths of the 2^(L′) states of thetrellis diagram (i.e., the most inexpensive and most probable statetransitions in each case), which, however, are different from state tostate as a consequence, so that a correspondingly high computationaleffort and storage requirement is needed since the equalizer must carrya list with 2^(L′) states at each time μ.

SUMMARY OF THE INVENTION

The presently disclosed method and apparatus provide generation ofinformation for channel decoding in a radio receiver wherein thecomputational expenditure and the storage space needed for computationsare reduced.

Specifically, a method for generating reliability information forchannel decoding in a radio receiver is disclosed wherein reliabilityinformation (q) specifies probabilities of a data symbol (z) received bythe radio receiver via a radio channel based on one of first and secondvalues that are transmitted. The method includes determining reliabilityinformation (q) for a time μ−L′ at an arbitrary time μfor each receiveddata symbol (z) by determining through the use of a state model having2^(L)′ states a first path that most probably contains the first valueat time μ−L′. Also a second path which most probably contains a secondvalue at time μ−L′ is determined and metrics calculated for the firstpath and the second path are placed into a relationship with one anotherwherein metrics calculated for the first path and the second path arecalculated in dependence on a first group of symbols of the state modelpresent at time μ . . . μ−L′ and a second group of symbols of the statemodel present at times μ−L′−1 . . . μ−L and L corresponding to at leastthe length of a channel impulse response of the radio channel with Lgreater than L′. Furthermore, the method includes the step of utilizinga value that has been decided before the time μ−L′ and is identical forall states of the state model as symbols of the second group fordetermining the reliability information (q) for time μ−L′.

An apparatus for use in a radio receiver is also disclosed that includesan equalizer configured to equalize a radio signal received via a radiochannel and generate reliability information (q) for a downstreamchannel decoder, wherein the reliability information (q) specifiesprobabilities of a received data symbol (z) based on at least one of afirst and second transmitted value. The equalizer also determines foreach received data symbol (z) at an arbitrary time μ a reliabilityinformation item (q) for a time μ−L′ by determining, by means of a statemodel with 2^(L′) states, a first path that most probably contains thefirst value at time μ−L′ and a second path that most probably containsthe second value at time μ−L′. The equalizer further places metricscalculated for the first path and the second path in relationship withone another. Additionally, the equalizer calculates the metricscalculated for the first path and the second path in dependence on afirst group of symbols of the state model present at times μ . . . μ−L′and a second group of symbols of a state model present at times μ−L′−1 .. . μ−L, with L corresponding to at least the length of the channelimpulse response and the radio channel with L greater than L′. Finally,the equalizer is also configured to utilize the value of the reliabilityinformation that was decided before time μ−L′ and is identical for allstates of the state model as symbols of the second group in order todetermine the reliability information (q) for the time μ−L′ symbols.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a mobile radio transmission model in the form of asimple block diagram;

FIG. 2 illustrates a model of the channel of the mobile radio systemshown in FIG. 1; and

FIGS. 3A and 3B illustrate representations for explaining thecalculation of metrics in a trellis diagram.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

To derive and explain the principles used in the method and apparatusconstructed in accordance with the teachings of the present invention,reference is made in the text that follows to the aforementioned printeddocument “Optimum and Sub-optimum Detection of Coded Data Disturbed byTime-varying Intersymbol Interference”, Wolfgang Koch and Alfred Baier,1990 IEEE, and, in particular, to the transmission model of a mobileradio system shown in FIG. 1, which includes a mobile radio transmitter1 and a mobile radio receiver 7 that communicate with one another via aRF channel 6.

In the transmitter 1, information to be transmitted (e.g., voiceinformation) is first converted into a digital signal (i.e., into asequence of ‘1’ and ‘0’ symbols) by a source encoder 2 and output in theform of source-coded data words or data vectors b, the individualelements or symbols of which in each case have the value ‘1’ or ‘0’. Achannel encoder 3 maps each data word b into a code word c, the symbolsof which are permuted (i.e., interleaved by an interleaver 4). Ideally,the interleaving is carried out in such a manner that any two symbols ofa code word c are mapped to two different output words d of theinterleaver 4. Finally, a formatter 5 adds L known symbols (so-called“tail” symbols) to the beginning and end of each data word d of theinterleaver 4 to provide a defined beginning and end state of the modelof the channel explained in greater detail in the text to follow. Thus,the formatter 5 outputs transmit words or transmit vectors e, wheree=(e_(1−L), . . . , e1, e2, . . . , e_(M)) and M=I+L, where I is thelength of the data words d output by the interleaver 4.

The RF channel 6 shown in FIG. 1 comprises, among other things, amodulator and amplifier of a transmitter (not shown), the actual RFchannel (transmission channel) and a receiver input stage (not shown),including an input filter and an A/D converter of the receiver and canbe represented by the RF channel model 6 shown in FIG. 2. The channelmodel corresponds to a state machine having L storage stages, theindividual temporarily stored transmit symbols e_(m . . .) e_(m−L) ofwhich are added with the aid of an adder 13 via a multiplier 12. Thecoefficients h₀ . . . h_(L) correspond to the coefficients of thechannel impulse response. The model also takes account of the noiseoccurring in the transmission channel in the form of the additive whiteGaussian noise (AWGN) which is superimposed on the output signal of theadder 13 with the aid of an adder 14 so that, finally, a received symbolz_(m) of the receiver is obtained.

Turning back to FIG. 1, receiver 7, equalizer 8, deinterleaver 9 andchannel decoder 10 jointly have the task of determining the originaltransmit sequence b with the greatest possible reliability z by means ofthe received sequence z. For this purpose, reliability information isgenerated with the aid of a soft decision for the channel decoder 10,the reliability information specifying for each received symbol the apriori probability that the received symbol is based on a transmitted‘0’ or ‘1’.

To generate this reliability information, the channel model shown inFIG. 2 may be represented by a corresponding trellis diagram thatdescribes the behavior of the channel in the form of state transitions.In this arrangement, the trellis specifies for each instantaneous stateof the channel a new state in dependence on a new symbol e_(m), atrellis state at time μ being designated by S_(μ) and being defined byS_(μ)=(e_(μ−L+1), . . . , e_(μ)), as will be explained in the text thatfollows.

Each change of state S_(μ−1)→S_(μ) can be assigned a metric incrementwhich is then evaluated later for assessing the probability of thischange in state and is defined by the following formula: $\begin{matrix}{{\lambda \left( {S_{\mu - 1},S_{\mu}} \right)} = {{{z_{\mu} - {\sum\limits_{l = 0}^{L}{e_{\mu - l}h_{l}}}}}^{2} \div \sigma^{2}}} & (1)\end{matrix}$

Alternatively, the following so-called “matched-filter” metric asdescribed in “Digital Communications”, Proakis, J. G., McGraw-Hill, NewYork, 1983, for example, may also be used: $\begin{matrix}{{\lambda \left( {S_{\mu - 1},S_{\mu}} \right)} = {{- {Re}}{\left\{ {e_{\mu}\left( {y_{\mu} - {\sum\limits_{l = 1}^{L}{e_{\mu - l}h_{l}}}} \right)} \right\} \div \sigma^{2}}}} & (2)\end{matrix}$

The expression $\begin{matrix}{y_{\mu} = {\sum\limits_{l = 0}^{L}{z_{\mu + l}h_{l}}}} & (3)\end{matrix}$

designates the output value of the matched-filter at time μ and theexpression: $\begin{matrix}{\rho_{l} = {{\sum\limits_{i = 0}^{L - 1}{h_{i + l}h_{i}\quad {for}\quad l}} = {0\quad \ldots \quad L}}} & (4)\end{matrix}$

designates the 1-th sample of the autocorrelation function of thechannel impulse response. However, the presently disclosed method andapparatus are not restricted to using the “matched-filter” metric.

Using the metric formula (2) described above, the trellis correspondingto the channel 6 shown in FIG. 2 can now be evaluated in order tocalculate corresponding probabilities for each state S_(μ) for eachtrellis or time interval μ. The procedure ideally passes both in thereverse direction and in the forward direction through the trellis. Inthe text that follows, the algorithm for processing a received word zwill be explained in greater detail.

When passing through the trellis in the reverse direction, a reversemetric Λ_(b)(S_(μ)) can be calculated for each trellis step μ from μ=Mto μ=L and for each trellis state S_(μ), using the following recursion:

Λ_(b)(S _(μ−1))=−1n{exp(−Λ _(b)(S′ _(μ))−λ(S _(μ−1) ,S′ _(μ))+exp(−Λ)_(b)(S″ _(μ))−λ(S _(μ−1) ,S″ _(μ)))}  (5)

The two states S′_(μ) and S″_(μ) are defined by the state S_(μ−1) in thepresence of state value e_(μ)=1 or e_(μ)=0 respectively.

Analogously, a forward metric Λ_(f) (S_(μ)) can be calculated for eachtrellis state S_(μ) using the following recursion:

Λ_(f)(S _(μ))=−1n{exp(−Λ _(f)(S′ _(μ−1))−λ(S′ _(μ−1) , S_(μ)))+exp(−Λ_(f)(S″ _(μ−1))−λ(S″ _(μ−1) , S _(μ)))}  (6)

In this case, the two states S′_(μ−1) and S″_(μ−1) are defined by thestate S_(μ) in the presence of the state value e_(μ−L)=1 or e_(μ−L)=0respectively.

For each state transition S_(μ−1)→S_(μ) of the trellis, the metricsΛ_(f)(S_(μ−1)), λ(S_(μ−1), S_(μ)) and Λ_(b)(S_(μ)) can now be added andtheir inverse exponentials can be added together separately fore_(μ−L)=0 and e_(μ−L)=1 over all states S_(μ): $\begin{matrix}{{u\left( e_{\mu - L} \right)} = {{- \ln}\left\{ {\sum\limits_{S_{\mu}}{\exp \left( {{- {\Lambda_{f}\left( S_{\mu - 1} \right)}} - {\lambda \left( {S_{\mu - 1},S_{\mu}} \right)} - {\Lambda_{b}\left( S_{\mu} \right)}} \right)}} \right\}}} & (7)\end{matrix}$

Using the expression shown in formula (7), finally, a soft decisionvalue q(e_(μ−L)) can be calculated for bit e_(μ−L) at time μ by puttingthe values calculated for e_(μ−L)=1 and e_(μ−L)=0 by means of theformula (7) in relation to one another:

q(e _(μ−L))=u(e _(μ−L)=1)−μ(e _(μ−L)=0)  (8)

To clarify the above formula (7), a section of the trellis for L=2 ande_(m)=1, associated with the channel model shown in FIG. 2, is shown inFIG. 3A and a section of the same trellis for e_(m)=0 is shown in FIG.3B. Both in FIG. 3A and in FIG. 3B, only those paths of the trellis areshown that contribute to the sum of the formula (7) in this example.Furthermore, the metrics Λ_(f(S) _(μ−1)), λ(S_(μ−1,) S_(μ)) andΛ_(b)(S_(μ)) are in each case entered in FIGS. 3A and 3B.

Since optimum soft decision values can be obtained as reliabilityinformation for the channel decoding with the aid of the proceduredescribed above, this algorithm is called “optimum soft decisionequalization algorithm” (OSDE), in “Optimum and Sub-Optimum Detection ofCoded Data Disturbed by Time-varying Intersymbol Interference”, WolfgangKoch and Alfred Baier, 1990 IEEE.

Since, however, this algorithm requires a lot of storage space and greatcomputational effort, there is a need for a simplified algorithm, thecomplexity of which, on the one hand, is distinctly reduced, and which,on the other hand, still supplies the most accurate reliabilityinformation possible.

With respect to this, it is first proposed in “Optimum and Sub-OptimumDetection of Coded Data Disturbed by Time-varying IntersymbolInterference”, Wolfgang Koch and Alfred Baier, 1990 IEEE, to simplifythe exponential calculations in the formula (7). Formula (7) generallycontains an expression of the form −1n(e^(−x)+e^(−y)), the followingrelation holding true, however, for such expressions:

−1n(e ^(−x) +e ^(−y))=min(x, y)−1n(1+e ^(−|y−x|))  (9)

For x<<y and x>>y, the expression −1n(e^(−x)+e^(−y)) can thus beapproximated with negligible error by forming the minimal value min(x,y). A further simplification can be achieved if passing through thetrellis in the reverse direction is omitted and the metrics Λ_(b)(S_(μ))in the formula (7) are thus set to 0 for all states S_(μ).

The calculation of the reliability information at time μ for the timeμ−L according to formula (8) is thus simplified as follows:$\begin{matrix}{{q\left( e_{\mu - L} \right)} = {{\min\limits_{S_{\mu}{e_{\mu - L}}^{= 1}}\left( {{\Lambda_{f}\left( S_{\mu - 1} \right)} + {\lambda \left( {S_{\mu - 1},S_{\mu}} \right)}} \right)} - {\min\limits_{S_{\mu}{e_{\mu - L}}^{= 0}}\left( {{\Lambda_{f}\left( S_{\mu - 1} \right)} + {\lambda \left( {S_{\mu - 1},S_{\mu}} \right)}} \right)}}} & (10)\end{matrix}$

The essential difference from the traditional Viterbi algorithm thusmerely consists in that only the selection of two minimum values from aset of 2^(L) metrics is required for calculating the soft decisionvalues q(e_(μ−L)). In “Optimum and Sub-optimum Detection of Coded DataDisturbed by Time-Varying Intersymbol Interference”, Wolfgang Koch andAlfred Baier, 1990 IEEE, this algorithm is therefore called “SoftDecision Viterbi Equalizer” (SDVE).

A further simplification can be achieved if this SDVE algorithm with2^(L) states is replaced by an SDVE algorithm with a reduced number ofstates 2^(L′) of the trellis with L′<L. The following expression is thenobtained for the above formula (2) for calculating the matched-filtermetrics: $\begin{matrix}{{\lambda^{\prime}\left( {S_{\mu - 1},S_{\mu}} \right)} = {{- {Re}}{\left\{ {e_{\mu}\left( {y_{\mu} - {\sum\limits_{l = 1}^{L^{\prime}}{e_{\mu - l}\rho_{l}}} - {\sum\limits_{l = {L^{\prime} + 1}}^{L}{e_{\mu - l}^{\prime}\rho_{l}}}} \right)} \right\} \div \sigma^{2}}}} & (11)\end{matrix}$

Bits e_(μ−1) for 1=1 . . . L′ then represent the state bits of the stateS_(μ−1) as in the above algorithm. In addition, the calculation of thematched-filter metrics is now also dependent on a second group of bitse′_(μ−1) for 1=L′+1 . . . L that are not directly included in thetrellis-based equalization, but instead, are decision feedback bits.

Whereas “Optimum and Sub-optimum Detection of Coded Data Disturbed byTime-Varying Intersymbol Interference”, Wolfgang Koch and Alfred Baier,1990 IEEE, proposes with respect to this group of decision feedback bitsto determine bits e′_(μ−1) for 1=L′+1 . . . L by means of the so-called“survivor” paths for the 2^(L′) states of the trellis (compare the abovedescription), it is proposed in the present method and apparatus not touse any state-dependent bits for this and, instead, use bits decidedpreviously (i.e., symbols, the value of which has previously beendefined as ‘0’or ‘1’) that have the same value for all 2^(L′) states.

These decision feedback bits e′_(μ−1) for 1=L′+1 . . . L can be obtainedby determining a reliability information item for the symbol at timeμ−L′ from the best zero path and the best one path as normal at everytime μ. This reliability information is converted into a bit having thevalue 0 or 1 by means of a fixed decision threshold (i.e., with the aidof a hard decision) and used as decision feedback bit for the furtherL−L′ times. Bits e′_(μ−L′−1) . . . e′_(μ−L) are thus constant for all2^(L′) states as a result of which less storage space and computationaleffort is required for calculating the matched-filter metrics accordingto formula (10). The above formulae do not need to be modified forcarrying out the disclosed method and apparatus.

The soft decision reliability information q(e_(μ−L′)) can then becalculated analogously to the above formula (10) if the expression offormula (11) is inserted into the formula (10): $\begin{matrix}{{q\left( e_{\mu - L^{\prime}} \right)} = {{\min\limits_{S_{\mu}{e_{\mu - L^{\prime}}}^{= 1}}\left( {{\Lambda_{f}\left( S_{\mu - 1} \right)} + {\lambda^{\prime}\left( {S_{\mu - 1},S_{\mu}} \right)}} \right)} - {\min\limits_{S_{\mu}{e_{\mu - L^{\prime}}}^{= 0}}\left( {{\Lambda_{f}\left( S_{\mu - 1} \right)} +^{\quad \prime}\left( {S_{\mu - 1},S_{\mu}} \right)} \right)}}} & (12)\end{matrix}$

At every time μ, a reliability information item is, therefore,calculated for time μ−L′ by using a reduced number of 2^(L′) differentstates. The disclosed method and apparatus are, thus, suboptimalapproaches of an SDVE algorithm with a reduced number of states.

As discussed, the present disclosure is based on the procedure describedin “Optimum and Sub-optimum Detection of Coded Data Disturbed byTime-varying Intersymbol Interference”, Wolfgang Koch and Alfred Baier,1990 IEEE and explained above. According to the method and apparatusconstructed in accordance with the teachings of the present invention,however, the bits taken into consideration in the form of a decisionfeedback are no longer carried in a state-dependent manner in a list butfor the decision feedback, symbols are included in the calculation thathave already been decided before (i.e., these bits or symbols areidentical for all 2^(L′) states).

To decide these symbols (i.e., to determine the binary value of thesesymbols, for example) a reliability information is generated for thesymbol at time μ−L′ from the best zero and one path at every time μ,converted into a bit having the corresponding value with the aid of ahard decision and used further in the calculation of the individualmetrics during the next L−L′ subsequent times.

The disclosed method and apparatus, which, in particular, can be usedfor equalizing intersymbol interference in mobile radio systemsaccording to the GSM, DCS1800 or PCAS1900 standard, have the greatadvantage that the implementation effort can be distinctly reduced, bothin a hardware implementation and in an implementation on a digitalsignal processor. This is due to the fact that no elaborate fields needto be carried for the decision feedback bits, but only L−L′ variablesare sufficient, needing only to be updated once per symbol output sothat both storage space and computing power, and thus electrical power,can be saved. In addition, extensive simulation (which has been carriedout, for example, for L′=2 on a GSM downlink) has proven that nonoticeable deterioration in the bit error rate can be found for thechannel models specified in the so-called GSM Recommendation 05.

The foregoing description has been presented for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the teachings of the invention to the exemplary embodimentsdisclosed. Many modifications and variations are possible in light ofthe above teachings and it is intended that the scope of this patent belimited not by this detailed description, but rather by the claimsappended hereto, either literally or under the doctrine of equivalents.

What is claimed is:
 1. A method for generating reliability information(q) for channel decoding in a radio receiver, the reliabilityinformation (q) specifying, for a time μ−L′ at an arbitrary time μ foreach data symbol (z) received by the radio receiver via a radio channel,the probability that the received data symbol (z) is one of a first anda second value based on one of a first and a second transmitted value,the method comprising: determining a first path that most probablycontains the first value at time μ−L′ and a second path that mostprobably contains the second value at time μ−L′ according to a statemodel having 2^(L′) states; calculating metrics for the first path andthe second path in dependence on a first group of symbols of the statemodel present at times μ . . . μ−L′ and a second group of symbols of thestate model present at times μ−L′−1 . . . μ−L, L corresponding to atleast a length of a channel impulse response of the radio channel withL>L′, symbols of the second group comprising values that have beendecided before the time μ−L′ and are identical for all states of thestate model; and placing the metrics calculated for the first path andthe second path into a relationship with one another.
 2. The methodaccording to claim 1, further comprising converting reliabilityinformation (q) determined for time μ into a corresponding binary symbolfor the time μ−L′, said binary symbol included in the second group ofsymbols during subsequent L−L′ times.
 3. The method according to claim2, wherein converting the reliability information (q) determined at timeμ comprises using a hard decision for the time μ−L′.
 4. The methodaccording to claim 4, wherein the state model comprises a trellisrepresentation.
 5. The method according to claim 1, wherein the metricscomprise matched-filter metrics.
 6. The method according to claim 5,further comprising calculating a metric λ′ for a transition from a stateS_(μ=1) to a state S_(μ)in the state model at time μ in accordance withthe following formula:${\lambda^{\prime}\left( {S_{\mu - 1},S_{\mu}} \right)} = {{- {Re}}{\left\{ {e_{\mu}\left( {y_{\mu} - {\sum\limits_{l = 1}^{L^{\prime}}{e_{\mu - l}\rho_{l}}} - {\sum\limits_{l = {L^{\prime} + 1}}^{L}{e_{\mu - 1}^{\prime}\rho_{l}}}} \right)} \right\} \div \sigma^{2}}}$

where e_(μ)designates symbols of the first group of symbols at time μ,e′_(μ) designates symbols of the second group of symbols at time μ,y_(μ)designates the output symbol of the matched-filter used at time μ,σ² designates the variance of the noise power density of the radiochannel and ρ₁ designates the lth value of the autocorrelation functionof the channel impulse response of the radio channel.
 7. The methodaccording to claim 1, wherein the method is used in a mobile radioreceiver of a mobile radio system.
 8. The method according to claim 7,wherein the method is used in a GSM mobile radio receiver.
 9. A radioreceiver comprising: an equalizer to generate reliability information(q), the reliability information (q) specifying, for a time μ−L′ at anarbitrary time μ for each data symbol (z) received by the radio receivervia a radio channel, the probability that the received data symbol (z)is one of a first and a second value based on one of a first and asecond transmitted value, the equalizer configured to: (a) determine afirst path that most probably contains the first value at time μ−L′ anda second path that most probably contains the second value at time μ−L′according to a state model having 2^(L′) states; (b) calculate metricsfor the first path and the second path in dependence on a first group ofsymbols of the state model present at times μ . . . μ−L, L and a secondgroup of symbols of the state model present at times μ−L′−1 . . . μ−L, Lcorresponding to at least a length of a channel impulse response of theradio channel with L>L′, symbols of the second group comprising valuesthat have been decided before the time μ−L′ and are identical for allstates of the state model; and (c) place the metrics calculated for thefirst path and the second path into a relationship with one another; anda channel decoder operatively coupled to the equalizer and configuredto: (a) receive the reliability information (q) from the equalizer, and(b) determine whether a received symbol is the first or the second valueaccording to the reliability information (q) received from theequalizer.
 10. The radio receiver according to claim 9, the equalizerconfigured to convert reliability information (q) determined at time μinto a corresponding binary symbol for the time μ−L′ , said binarysymbol included in the second group of symbols during subsequent L−L′times.
 11. The radio receiver according to claim 10, the equalizerconfigured to use a hard decision for the time μ−L′ to convert thereliability information (q) determined at time μ comprises.
 12. Theradio receiver according to claim 9, wherein the state model comprises atrellis representation.
 13. The radio receiver according to claim 9,wherein the metrics comprise matched-filter metrics.
 14. The radioreceiver according to claim 13, wherein the equalizer is configured tocalculate a metric λ′ for a transition from a state S_(μ−1) to a stateS_(μ) in the state model at time μ in accordance with the followingformula:${\lambda^{\prime}\left( {S_{\mu - 1},S_{\mu}} \right)} = {{- {Re}}{\left\{ {e_{\mu}\left( {y_{\mu} - {\sum\limits_{l = 1}^{L^{\prime}}{e_{\mu - l}\rho_{l}}} - {\sum\limits_{l = {L^{\prime} + 1}}^{L}{e_{\mu - 1}^{\prime}\rho_{l}}}} \right)} \right\} \div \sigma^{2}}}$

where e_(μ) designated the first group of symbols at time μ, e′_(μ)designates the second group of symbols at time μ, Y_(μ) designates theoutput symbol of the matched-filter used at time μ, σ² designates thevariance of the noise power density of the radio channel and P₁designates the lth value of the autocorrelation function of the channelimpulse response of the radio channel.
 15. The radio receiver accordingto claim 9, wherein the radio receiver is a mobile radio receiver of amobile radio system.